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Precision Agriculture

, Volume 20, Issue 1, pp 138–156 | Cite as

Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the Cr–Cb color space

  • Tian-Hu LiuEmail author
  • Reza Ehsani
  • Arash Toudeshki
  • Xiang-Jun Zou
  • Hong-Jun Wang
Article

Abstract

In precision agriculture, identification of fruit in trees is important. Furthermore, it is also essential for estimating the yield, targeting the exact location for a harvesting robot and selectively harvesting the fruit. An elliptical boundary model-based machine vision algorithm was developed to identify immature and mature pomelo fruit in trees. In the proposed solution, the images were converted from RGB space to Y′CbCr space. Then, ordinary least-squares (OLS) was introduced in fitting implicit second order polynomials of elliptical boundary models in the Cr–Cb color space for segmenting immature green fruits, mature green, green partial white, green partial yellow and green partial red fruits. Those elliptical boundary models along with area opening mathematical morphology and diameter thresholding were applied in the identification procedure. The algorithm was tested on a set of 200 validation images acquired under natural illumination conditions. The results of the validation test showed that the total correct identification rate was 93.5%. The total false positive, missed rate, repeated rate and merged rate were equal to 8.2, 6.5, 10.2 and 10.6%, respectively. The proposed method performed better in detecting mature fruit, the color of which is different from green, than in detecting immature green fruit. On average, the segmenting time for 640 × 480 and 1280 × 960 images were 0.134 and 0.200 s, respectively and the total identification time for 640 × 480 and 1280 × 960 images were 0.240 and 0.362 s, respectively.

Keywords

Computer vision Pomelo Fruit detection Elliptical boundary model 

Notes

Acknowledgements

This research is co-supported by Guangdong Science and Technology Plan Project with Research Grant 2017A010102024, National Key R & D Plan of China with Research Grant 2017YFD0700100 and National Science Project of China with Research Grant 31571568.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of EngineeringSouth China Agricultural UniversityGuangzhouChina
  2. 2.School of EngineeringUniversity of CaliforniaMercedUSA

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